Bayesian self-organising map for Gaussian mixtures

IEE Proceedings: Vision, Image and Signal Processing

Volumn 148, Issue 4, 2001, Pages 234-240

  Yin, H ^a   Allinson, N M ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; SELF ORGANIZING MAPS;

BAYESIAN SELF ORGANISING MAP; EXPECTATION MAXIMISATION ALGORITHM;

PATTERN RECOGNITION;

EID: 0035435684     PISSN: 1350245X     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-vis:20010378     Document Type: Article

References (27)

1. A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions
2. Maximum likelihood from incomplete date via the EM algorithm
3. Mixture densities, maximum likelihood and the EM algorithm
4. Unsupervised learning by EM algorithm based on finite mixture of Gaussians
5. On convergence properties of the EM algorithm for Gaussian mixtures
6. Self-organised formation of topologically correct feature maps
7. Asymptotic level density for class of vector quantisation processes
8. On the distribution and convergence of feature space in self-organising maps
9. Competitive and self-organising algorithms based on the minimisation of an information criterion
10. Learning in artificial neural networks
11. Bayesian learning for self-organising maps
12. GTM: The generative topographic mapping
13. Supervised and unsupervised learning in radial-basis-function classifiers
14. On information and sufficiency
15. A stochastic approximation method
16. Bayesian inference in statistical analysis
17. Adaptive mixtures: Recursive nonparametric pattern recognition
18. Stochastic competitive learning
19. Self-organisation as an iterative kernel smoothing process